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Model Card of lmqg/mbart-large-cc25-dequad-qg

This model is fine-tuned version of facebook/mbart-large-cc25 for question generation task on the lmqg/qg_dequad (dataset_name: default) via lmqg.

Overview

Usage

from lmqg import TransformersQG

# initialize model
model = TransformersQG(language="de", model="lmqg/mbart-large-cc25-dequad-qg")

# model prediction
questions = model.generate_q(list_context="das erste weltweit errichtete Hermann Brehmer 1855 im niederschlesischen ''Görbersdorf'' (heute Sokołowsko, Polen).", list_answer="1855")
  • With transformers
from transformers import pipeline

pipe = pipeline("text2text-generation", "lmqg/mbart-large-cc25-dequad-qg")
output = pipe("Empfangs- und Sendeantenne sollen in ihrer Polarisation übereinstimmen, andernfalls <hl> wird die Signalübertragung stark gedämpft. <hl>")

Evaluation

Score Type Dataset
BERTScore 80.77 default lmqg/qg_dequad
Bleu_1 10.96 default lmqg/qg_dequad
Bleu_2 4.48 default lmqg/qg_dequad
Bleu_3 1.91 default lmqg/qg_dequad
Bleu_4 0.75 default lmqg/qg_dequad
METEOR 13.71 default lmqg/qg_dequad
MoverScore 55.88 default lmqg/qg_dequad
ROUGE_L 11.19 default lmqg/qg_dequad
  • Metric (Question & Answer Generation, Reference Answer): Each question is generated from the gold answer. raw metric file
Score Type Dataset
QAAlignedF1Score (BERTScore) 90.66 default lmqg/qg_dequad
QAAlignedF1Score (MoverScore) 65.36 default lmqg/qg_dequad
QAAlignedPrecision (BERTScore) 90.64 default lmqg/qg_dequad
QAAlignedPrecision (MoverScore) 65.37 default lmqg/qg_dequad
QAAlignedRecall (BERTScore) 90.69 default lmqg/qg_dequad
QAAlignedRecall (MoverScore) 65.36 default lmqg/qg_dequad
Score Type Dataset
QAAlignedF1Score (BERTScore) 0 default lmqg/qg_dequad
QAAlignedF1Score (MoverScore) 0 default lmqg/qg_dequad
QAAlignedPrecision (BERTScore) 0 default lmqg/qg_dequad
QAAlignedPrecision (MoverScore) 0 default lmqg/qg_dequad
QAAlignedRecall (BERTScore) 0 default lmqg/qg_dequad
QAAlignedRecall (MoverScore) 0 default lmqg/qg_dequad

Training hyperparameters

The following hyperparameters were used during fine-tuning:

  • dataset_path: lmqg/qg_dequad
  • dataset_name: default
  • input_types: ['paragraph_answer']
  • output_types: ['question']
  • prefix_types: None
  • model: facebook/mbart-large-cc25
  • max_length: 512
  • max_length_output: 32
  • epoch: 11
  • batch: 4
  • lr: 0.0001
  • fp16: False
  • random_seed: 1
  • gradient_accumulation_steps: 16
  • label_smoothing: 0.15

The full configuration can be found at fine-tuning config file.

Citation

@inproceedings{ushio-etal-2022-generative,
    title = "{G}enerative {L}anguage {M}odels for {P}aragraph-{L}evel {Q}uestion {G}eneration",
    author = "Ushio, Asahi  and
        Alva-Manchego, Fernando  and
        Camacho-Collados, Jose",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, U.A.E.",
    publisher = "Association for Computational Linguistics",
}
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Dataset used to train research-backup/mbart-large-cc25-dequad-qg

Evaluation results

  • BLEU4 (Question Generation) on lmqg/qg_dequad
    self-reported
    0.750
  • ROUGE-L (Question Generation) on lmqg/qg_dequad
    self-reported
    11.190
  • METEOR (Question Generation) on lmqg/qg_dequad
    self-reported
    13.710
  • BERTScore (Question Generation) on lmqg/qg_dequad
    self-reported
    80.770
  • MoverScore (Question Generation) on lmqg/qg_dequad
    self-reported
    55.880
  • QAAlignedF1Score-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_dequad
    self-reported
    90.660
  • QAAlignedRecall-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_dequad
    self-reported
    90.690
  • QAAlignedPrecision-BERTScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_dequad
    self-reported
    90.640
  • QAAlignedF1Score-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_dequad
    self-reported
    65.360
  • QAAlignedRecall-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_dequad
    self-reported
    65.360
  • QAAlignedPrecision-MoverScore (Question & Answer Generation (with Gold Answer)) [Gold Answer] on lmqg/qg_dequad
    self-reported
    65.370
  • QAAlignedF1Score-BERTScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_dequad
    self-reported
    0.000
  • QAAlignedRecall-BERTScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_dequad
    self-reported
    0.000
  • QAAlignedPrecision-BERTScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_dequad
    self-reported
    0.000
  • QAAlignedF1Score-MoverScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_dequad
    self-reported
    0.000
  • QAAlignedRecall-MoverScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_dequad
    self-reported
    0.000
  • QAAlignedPrecision-MoverScore (Question & Answer Generation) [Gold Answer] on lmqg/qg_dequad
    self-reported
    0.000